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dc.creatorSerrano Gotarredona, María Teresaes
dc.creatorLinares Barranco, Bernabées
dc.date.accessioned2018-06-26T13:45:00Z
dc.date.available2018-06-26T13:45:00Z
dc.date.issued1996
dc.identifier.citationSerrano Gotarredona, M.T. y Linares Barranco, B. (1996). A modified ART 1 algorithm more suitable for VLSI implementations. Neural Networks, 9 (6), 1025-1043.
dc.identifier.issn0893-6080es
dc.identifier.urihttps://hdl.handle.net/11441/76479
dc.description.abstractThis paper presents a modification to the original ART 1 algorithm (Carpenter and Grossberg, 1987a, A massively parallel architecture for a self-organizing neural pattern recognition machine, Computer Vision, Graphics, and Image Processing, 37, 54–115) that is conceptually similar, can be implemented in hardware with less sophisticated building blocks, and maintains the computational capabilities of the originally proposed algorithm. This modified ART 1 algorithm (which we will call here ART 1m) is the result of hardware motivated simplifications investigated during the design of an actual ART 1 chip [Serrano-Gotarredona et al., 1994, Proc. 1994 IEEE Int. Conf. Neural Networks (Vol. 3, pp. 1912–1916); Serrano-Gotarredona and Linares-Barranco, 1996, IEEE Trans. VLSI Systems, (in press)]. The purpose of this paper is simply to justify theoretically that the modified algorithm preserves the computational properties of the original one and to study the difference in behavior between the two approaches.es
dc.formatapplication/pdfes
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofNeural Networks, 9 (6), 1025-1043.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleA modified ART 1 algorithm more suitable for VLSI implementationses
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/submittedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadoreses
idus.format.extent30 p.es
dc.journaltitleNeural Networkses
dc.publication.volumen9es
dc.publication.issue6es
dc.publication.initialPage1025es
dc.publication.endPage1043es

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